The primary goal is the development of an integrated computer environment for the analysis and display of biomagnetic signals recorded from pregnant women including anatomical information obtained by three dimensional ultrasound. This proposal is a development of the combined efforts of different members of the University of Arkansas for Medical Sciences (UAMS), the University of Arkansas at Little Rock (UALR), Scientific Computing and Imaging Institute (University of Utah), and CTF Systems, Inc. The need for the establishment of such an effort is based on the demanding problems in the field of fetal assessment and the increased availability of objective measurements for fetal and maternal physiology. At UAMS the world's first biomagnetic-sensing system, SARA (SQUID Array for Reproductive Assessment), is installed for this purpose. This system consists of 151 primary superconducting sensors, which detect biomagnetic fields generated in the body by various bioelectric sources such as the maternal and fetal heart, fetal brain, uterine muscles. The primary data generated with this system is a high dimensional spatial-temporal dataset. We propose to link this physiological measure to three dimensional ultrasound to obtain the appropriate anatomical information necessary to improve the interpretation and display of biomagnetic signals in a common frame of reference. The three major goals of the proposal are: (1) the generation of a biomagnetic model for the propagation of electrical and magnetic signals in the abdomen.(2) the development of efficient separation algorithms for the spatial-temporal datasets. (3) the localization of the generating sources. [unreadable] [unreadable] These data analysis tools will help us to optimize our auditory and visual stimulation protocols for standard evoked field or steady state evoked field measurements. The new computational tools must be programmed, validated, and must execute rapidly. This demand can only be achieved if efficient algorithms are available for processing potentially large spatial-temporal datasets. In the future, the device, analysis tools, and datasets should be accessible to outside researchers for their own studies and collaborating efforts, by means of secure and fast Internet access.